Generative Adversarial Networks and Deep Learning: Theory and Applications

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(as of Dec 24,2024 09:22:55 UTC – Details)

ASIN ‏ : ‎ B0BXYVR2KF
Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (April 10, 2023)
Publication date ‏ : ‎ April 10, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 7070 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 222 pages
Page numbers source ISBN ‏ : ‎ 1032068108

Generative Adversarial Networks (GANs) and Deep Learning have revolutionized the field of artificial intelligence in recent years. In this post, we will delve into the theory behind GANs and explore their various applications in different domains.

GANs are a class of machine learning algorithms that are used to generate new data samples from existing datasets. They consist of two neural networks, the generator and the discriminator, that are trained simultaneously in a competitive manner. The generator generates fake data samples, while the discriminator tries to distinguish between real and fake samples. Through this adversarial training process, the generator learns to generate realistic data samples that are indistinguishable from the real ones.

One of the key advantages of GANs is their ability to generate high-quality, diverse data samples, such as images, text, and even music. This has led to a wide range of applications in various fields, including image generation, image-to-image translation, style transfer, and data augmentation.

In addition to image generation, GANs have also been used in anomaly detection, semi-supervised learning, and reinforcement learning. They have been applied in healthcare for medical image analysis, in finance for fraud detection, and in creative industries for art generation.

Overall, GANs and Deep Learning have opened up new possibilities in artificial intelligence and have paved the way for exciting advancements in various domains. As researchers continue to explore the potential of these technologies, we can expect to see even more innovative applications in the future.
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